Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std plot

beta_std_plot

beta_std_plot

Metadata

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}

LDSC

*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call: 
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18/UKB-b-18_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18/UKB-b-18_data.vcf.gz ...
Read summary statistics for 9113259 SNPs.
Dropped 9234 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1287454 SNPs remain.
After merging with regression SNP LD, 1287454 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.014 (0.004)
Lambda GC: 1.0229
Mean Chi^2: 1.0281
Intercept: 0.9973 (0.0064)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:58 2019
Total time elapsed: 1.0m:40.38s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.948,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 98885,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1287454,
    "ldsc_nsnp_merge_regression_ld": 1287454,
    "ldsc_observed_scale_h2_beta": 0.014,
    "ldsc_observed_scale_h2_se": 0.004,
    "ldsc_intercept_beta": 0.9973,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0229,
    "ldsc_mean_chisq": 1.0281,
    "ldsc_ratio": -0.0961
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
ldsc_intercept_beta FALSE
n_clumped_hits FALSE
r2_sum1 FALSE
r2_sum2 FALSE
r2_sum3 FALSE
r2_sum4 FALSE

Definitions

General metrics

  • af_correlation: Correlation coefficient between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations for <*> column.

se_n metrics

  • n_est: Estimated sample size value, \(\widehat{n}\).
  • ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expect ratio_se_n to be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.
  • mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.
    • \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
    • \({z}_j = \frac{\beta_j}{{se}_j}\),
    • and \(\beta_j\) is the reported effect size.
  • ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean of diff and the mean of diff2 (expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.
    • \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
    • \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
  • sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.
    • \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
    • \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
    • and \({se}_j\) is the reported standard error.
  • sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.
    • \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
    • \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),

r2 metrics

Sum of variance explained, calculated from the clumped top hits sample.

  • r2_sum<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),
    • 3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),
    • 4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).

LDSC metrics

Metrics from LD regression

  • ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.
  • ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.
  • ldsc_observed_scale_h2_{beta,se} Coefficient value and SE for total observed scale h2.
  • ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.
  • ldsc_lambda_gc: Lambda GC statistics.
  • ldsc_mean_chisq: Mean \(\chi^2\) statistics.
  • ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).

Flags

When a metric needs attention, the flag should return TRUE.

  • af_correlation: abs(af_correlation) < 0.7.
  • inflation_factor: inflation_factor > 1.2.
  • n: n (max reported sample size) < 10000.
  • is_snpid_non_unique: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_sum<*>: r2_sum<*> > 0.5

Plots

  • Manhattan plot
    • Red line: \(-log_{10}^{5 \times 10^{-8}}\)
    • Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
  • QQ plot
  • AF plot
  • P-Z plot
  • beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)

Diagnostics

Details

Summary stats

skim_type skim_variable n_missing complete_rate character.min character.max character.empty character.n_unique character.whitespace logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 9104068 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9113259 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.640349e+00 5.756769e+00 1.000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.879286e+07 5.632621e+07 828.000000 3.245546e+07 6.936306e+07 1.145258e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.050000e-05 5.639000e-03 -0.069656 -2.136700e-03 1.800000e-06 2.154100e-03 5.695480e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 4.469800e-03 3.358400e-03 0.001582 1.889700e-03 2.926000e-03 6.135200e-03 3.611850e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.954072e-01 2.900490e-01 0.000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.954060e-01 2.900219e-01 0.000000 2.431327e-01 4.939533e-01 7.463082e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.178254e-01 2.582751e-01 0.003093 1.935200e-02 9.818200e-02 3.431635e-01 9.969070e-01 ▇▂▁▁▁
numeric AF_reference 98885 0.9891493 NA NA NA NA NA NA NA 2.183281e-01 2.501412e-01 0.000000 1.657350e-02 1.160140e-01 3.418530e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C 0.0067835 0.0029061 0.0200000 0.0195825 0.623895 0.7821490 NA
1 54676 rs2462492 C T -0.0004497 0.0028804 0.8800001 0.8759243 0.399427 NA NA
1 86028 rs114608975 T C -0.0046441 0.0046161 0.3100002 0.3143808 0.103378 0.0277556 NA
1 91536 rs6702460 G T -0.0006903 0.0028359 0.8100000 0.8076889 0.456523 0.4207270 NA
1 234313 rs8179466 C T 0.0141385 0.0056372 0.0120000 0.0121397 0.074018 NA NA
1 534192 rs6680723 C T -0.0037357 0.0032342 0.2500000 0.2480602 0.241069 NA NA
1 546697 rs12025928 A G 0.0032439 0.0040306 0.4199997 0.4209307 0.913024 NA NA
1 693731 rs12238997 A G -0.0026065 0.0027138 0.3400001 0.3368139 0.116857 0.1417730 NA
1 705882 rs72631875 G A -0.0072091 0.0039556 0.0680002 0.0683775 0.067719 0.0315495 NA
1 706368 rs55727773 A G -0.0010691 0.0020114 0.5999997 0.5950684 0.515759 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0013654 0.0024370 0.5800000 0.5753082 0.136641 0.2052720 NA
22 51219387 rs9616832 T C 0.0000588 0.0031669 0.9900000 0.9851794 0.072779 0.0654952 NA
22 51219704 rs147475742 G A -0.0006656 0.0042573 0.8800001 0.8757533 0.041213 0.0473243 NA
22 51221190 rs369304721 G A 0.0013997 0.0042354 0.7400005 0.7410424 0.049192 NA NA
22 51221731 rs115055839 T C 0.0000103 0.0031693 1.0000000 0.9973977 0.072233 0.0625000 NA
22 51222100 rs114553188 G T -0.0046271 0.0037230 0.2099999 0.2139254 0.054253 0.0880591 NA
22 51223637 rs375798137 G A -0.0046372 0.0037418 0.2200002 0.2152423 0.053887 0.0788738 NA
22 51229805 rs9616985 T C 0.0001529 0.0031808 0.9599999 0.9616607 0.072059 0.0730831 NA
22 51232488 rs376461333 A G -0.0017765 0.0075229 0.8100000 0.8133215 0.019774 NA NA
22 51237063 rs3896457 T C -0.0003746 0.0019276 0.8499999 0.8458963 0.298322 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623895 ES:SE:LP:AF:ID  0.0067835:0.00290607:1.69897:0.623895:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399427 ES:SE:LP:AF:ID  -0.000449732:0.00288035:0.0555173:0.399427:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103378 ES:SE:LP:AF:ID  -0.00464409:0.00461606:0.508638:0.103378:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456523 ES:SE:LP:AF:ID  -0.000690292:0.00283594:0.091515:0.456523:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074018 ES:SE:LP:AF:ID  0.0141385:0.00563723:1.92082:0.074018:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241069 ES:SE:LP:AF:ID  -0.00373574:0.0032342:0.60206:0.241069:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913024 ES:SE:LP:AF:ID  0.00324389:0.00403064:0.376751:0.913024:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116857 ES:SE:LP:AF:ID  -0.00260654:0.00271379:0.468521:0.116857:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067719 ES:SE:LP:AF:ID  -0.00720907:0.00395557:1.16749:0.067719:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515759 ES:SE:LP:AF:ID  -0.00106906:0.00201138:0.221849:0.515759:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032973 ES:SE:LP:AF:ID  -0.0124443:0.0050778:1.85387:0.032973:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036593 ES:SE:LP:AF:ID  -0.0101754:0.00461:1.56864:0.036593:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03671  ES:SE:LP:AF:ID  -0.0105177:0.00459189:1.65758:0.03671:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036406 ES:SE:LP:AF:ID  -0.0104503:0.00462576:1.61979:0.036406:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01662  ES:SE:LP:AF:ID  -0.00500852:0.00707065:0.318759:0.01662:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036947 ES:SE:LP:AF:ID  -0.0103088:0.00457367:1.61979:0.036947:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037066 ES:SE:LP:AF:ID  -0.0106205:0.00455725:1.69897:0.037066:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101809 ES:SE:LP:AF:ID  0.00161327:0.00330946:0.200659:0.101809:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959293 ES:SE:LP:AF:ID  0.0103376:0.00440493:1.72125:0.959293:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031206 ES:SE:LP:AF:ID  0.011518:0.00808373:0.823909:0.031206:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053368 ES:SE:LP:AF:ID  -0.000212531:0.00633151:0.0132283:0.053368:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036561 ES:SE:LP:AF:ID  -0.0098492:0.0045864:1.49485:0.036561:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036869 ES:SE:LP:AF:ID  -0.0109315:0.00454541:1.79588:0.036869:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843061 ES:SE:LP:AF:ID  0.00483834:0.00235572:1.39794:0.843061:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055807 ES:SE:LP:AF:ID  -0.000754586:0.00381853:0.0757207:0.055807:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122806 ES:SE:LP:AF:ID  -0.00231555:0.00257639:0.431798:0.122806:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024824 ES:SE:LP:AF:ID  0.00792849:0.00644664:0.657577:0.024824:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122042 ES:SE:LP:AF:ID  -0.00260617:0.00257782:0.508638:0.122042:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132536 ES:SE:LP:AF:ID  -0.00492144:0.00254018:1.27572:0.132536:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011111 ES:SE:LP:AF:ID  -0.00732118:0.00926916:0.366532:0.011111:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005663 ES:SE:LP:AF:ID  -0.00872914:0.0120082:0.327902:0.005663:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036785 ES:SE:LP:AF:ID  -0.0103213:0.00449946:1.65758:0.036785:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838671 ES:SE:LP:AF:ID  0.00585492:0.00228107:2:0.838671:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838334 ES:SE:LP:AF:ID  0.0060738:0.00227879:2.11351:0.838334:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869261 ES:SE:LP:AF:ID  0.00399652:0.0024431:1:0.869261:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130388 ES:SE:LP:AF:ID  -0.00399065:0.00244854:1:0.130388:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037276 ES:SE:LP:AF:ID  -0.0116421:0.0044241:2.07058:0.037276:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037527 ES:SE:LP:AF:ID  -0.0118533:0.0043949:2.1549:0.037527:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868618 ES:SE:LP:AF:ID  0.00414807:0.00243864:1.05061:0.868618:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868728 ES:SE:LP:AF:ID  0.00417968:0.00243987:1.06048:0.868728:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037483 ES:SE:LP:AF:ID  -0.011592:0.00441506:2.06048:0.037483:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868626 ES:SE:LP:AF:ID  0.00416089:0.00243859:1.05552:0.868626:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005194 ES:SE:LP:AF:ID  -0.0205858:0.0124231:1.00877:0.005194:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005165 ES:SE:LP:AF:ID  -0.019899:0.012444:0.958607:0.005165:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837766 ES:SE:LP:AF:ID  0.0061113:0.00227246:2.14267:0.837766:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037521 ES:SE:LP:AF:ID  -0.0111691:0.00442007:1.92082:0.037521:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838368 ES:SE:LP:AF:ID  0.00608813:0.00227864:2.12494:0.838368:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013586 ES:SE:LP:AF:ID  -0.0107987:0.00799532:0.744727:0.013586:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.00548  ES:SE:LP:AF:ID  0.00153362:0.0122997:0.0457575:0.00548:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839512 ES:SE:LP:AF:ID  0.0053222:0.0023091:1.67778:0.839512:rs3131965